CN104217580B - Road network Semantic Modeling Method and system towards vehicle groups animation - Google Patents
Road network Semantic Modeling Method and system towards vehicle groups animation Download PDFInfo
- Publication number
- CN104217580B CN104217580B CN201410400159.2A CN201410400159A CN104217580B CN 104217580 B CN104217580 B CN 104217580B CN 201410400159 A CN201410400159 A CN 201410400159A CN 104217580 B CN104217580 B CN 104217580B
- Authority
- CN
- China
- Prior art keywords
- data
- lane line
- line
- lane
- road
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Processing Or Creating Images (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of road network Semantic Modeling Method towards vehicle groups animation and system, wherein the method comprises the steps, obtains lane line vector data；This lane line is carried out curve fitting and obtains lane line curve；This lane line curve is carried out Data Discretization compression and obtains the discrete broken line of lane line；Interrupting rule according to the discrete broken line of this lane line, broken line discrete to this lane line interrupts and merges；The node constituting this lane line off-line broken line is carried out semantic information assignment, obtains lane line semantic data, as atomic layer data；Calculate and store the connection between each track and the relation such as adjacent according to these atomic layer data, then generate the hiberarchy datas such as section, crossing, road, would know that the information such as signal lights distribution according to this hiberarchy data.
Description
Technical field
The present invention relates to Computer Simulation modeling field, model field particularly to traffic simulation.
Background technology
Vehicle motion is an important component part in Scene of Virtual City.In a large amount of three-dimensional emulation all
Need to incorporate vehicle simulation effect true to nature, such as virtual earth (Microsoft Virtual Earth), city engine
(city engine), application software and the game such as city life (urban life).Therefore, along with computer graphic
What shape learned a skill develops rapidly, and vehicle groups cartoon technique has attracted the research of many scholars.
The carrier that road network occurs as vehicle motor behavior, plays foundation in vehicle groups animation simulation
Effect.The most effective road network semantic model, is possible not only to be greatly improved quality and the effect of vehicle simulation emulation
Rate, it is also possible to reduce data input quantity and manual setting (such as different directions signal lights at grade crossing
Conflict coordination).
The most existing many methods for road network modeling.The most a part of method is to realize virtual field
The structure of scape.This class model is concerned only with the generation of road vectors data, the topological relation being indifferent between road,
So model cannot be used directly for vehicle movement simulation.In order to realize traffic simulation and movement navigation etc., one
A little scholars add topological Semantics information on the basis of geological information.This class model describes road in thicker dynamics
Net, the adjacent and annexation being focused mainly between road, when vehicle groups animation, exist as follows
Two outstanding problems: first, these models cannot realize respectively conflicting at crossing the automatic synchronization in direction, all
It is required for the data such as additional input signal lamp at grade crossing or processes these lines by manual setting
Conflicting in road, workload produces greatly and easily mistake；Secondly, model, when vehicle motion simulation, asks for car
Requiring to look up calculating and numerical value approximation computation when position coordinates and deflection, ratio is relatively time-consuming, and impact is the most dynamic
Draw the efficiency of emulation.
Why in summary, there are the problems referred to above in existing road network modeling method, is because these models one
As be that the data such as signal lights have been known for traffic simulation and forecast analysis.And traffic simulation master
Road section information to be paid close attention to, it is not necessary to the information such as concrete coordinate and deflection of knowing vehicle.And signal information
Automatically generate and obtain with vehicle coordinate deflection, be necessary to vehicle animation simulation.We study further
Find, the problem that existing road net model exists when vehicle animation simulation will be solved, can be fixed by extension
Justice lane line is semantic, and definition level semantic data thereon, it is possible to ensureing less data input quantity
On the premise of, preferably aid in vehicle animation simulation.
Summary of the invention
Not enough for prior art, the present invention proposes a kind of road network semantic modeling towards vehicle groups animation
Method and system, present invention aim to address that current road net model needs in vehicle animation simulation in a large number
Loaded down with trivial details data input and setting, and during vehicle animation simulation, obtain the problem that road net data is inefficient.
The present invention is capable of only inputting lane line vector data (any representation, can coarse can be finely), just
Can effectively meet the data message required for vehicle animation simulation.
For reaching above-mentioned purpose, the invention discloses a kind of road network semantic modeling side towards vehicle groups animation
Method, including: atomic layer data genaration step and hiberarchy data generation step, this atomic layer data genaration
Step includes:
Step 1, obtains lane line vector data；
Step 2, carries out curve fitting to this lane line vector data and obtains the curve data of correspondence；
Step 3, carries out this curve data Data Discretization compression and obtains the discrete folding that this curve data is corresponding
Line data；
Step 4, interrupts rule according to this lane line, and these discrete broken line data are interrupted and merged；
Step 5, carries out semantic information assignment by the node constituting this discrete broken line, obtains lane line semanteme number
According to (Lane), as atomic layer data；
This hiberarchy data generation step includes:
Step 6, based on these atomic layer data, according to the syntopy Automatic generation of information between this lane line
Section (Link)；
Step 7, based on these atomic layer data, automatically generates according to the connection relation information between this lane line
Junction point (Connector)；
Step 8, automatically generates road according to the overlapping relation between this section and this junction point and this lane line
Mouth (Intersection)；
Step 9, obtains road (Road) according to the annexation between this section.
Described lane line vector data includes regular vehicle diatom and road junction roadway line, and road junction roadway line refers to
The circuit that at crossing, wheeled route according to vehicle additionally adds；
Described curve matching typically uses the methods such as spline interpolation；
The described rule that interrupts is: average distance is identical in the line style of the different lane lines specified in threshold value, walk
To identical, length is equal；The out-degree and the in-degree that constitute the node within the point range of the discrete broken line of this lane line are divided
Deng Yu 1.
The semantic information of described imparting includes: this node away from this lane line discrete broken line starting point along this lane line from
Dissipate the total length of broken line, the vector direction angle that this node is constituted with rear adjacent node.
The described road network Semantic Modeling Method towards vehicle groups animation, these atomic layer data are used for defining and raw
Becoming hiberarchy data, this hiberarchy data includes, section, junction point, crossing, road；
This section refers to former generation's lane line type of lane line and those lane lines with identical geometry linear
Set for the lane line at crossing.
This crossing, refers to meet the set of the junction point of specified relationship, when this crossing generates, and the method that labelling can be used
Quickly generate.
The invention still further relates to a kind of road network semantic modeling system towards vehicle groups animation, it is characterised in that
Including, atomic layer data generation module and hiberarchy data generation module；This atomic layer data generation module
Including:
Data input module, is used for obtaining lane line vector data；
Curve fitting module, for this lane line vector data curve matching, obtaining the curve data of correspondence；
Data discrete module, this curve data carries out Data Discretization compression, and to obtain this curve data corresponding
Discrete broken line data；
Interrupt merging module, according to interrupting rule, discrete broken line data are interrupted and merge；
Semantic assignment module, obtains for the node constituting this lane line off-line broken line is carried out semantic information assignment
To lane line semantic data, as atomic layer data；
This hiberarchy data generation module includes:
Section generation module, based on these atomic layer data, according to the syntopy information between this lane line certainly
Dynamic generation section；
Junction point generation module, based on these atomic layer data, according to the connection relation information between this lane line
Automatically generate junction point；
Crossing generation module, automatic according to the overlapping relation between this section and this junction point and this lane line
Generate crossing；
Road generation module, obtains road according to the annexation between this section.
The described road network semantic modeling system towards vehicle groups animation, this lane line include regular vehicle diatom and
Road junction roadway line.
The described road network semantic modeling system towards vehicle groups animation, this interrupts rule and includes:
The line style of the average distance different lane lines in specifying threshold value is identical, move towards identical, and length is equal；
The out-degree and the in-degree that constitute the node within the point range of the discrete broken line of this lane line are respectively equal to 1.
The described road network semantic modeling system towards vehicle groups animation, the semantic information that this node comprises includes
From this discrete broken line starting point along this discrete broken line to the total length of this point and adjacent 2 constitute vector
Deflection.
The described road network semantic modeling system towards vehicle groups animation, these atomic layer data are used for defining and raw
Becoming this hiberarchy data, this hiberarchy data includes, section, junction point, crossing, road；This section
Refer to that there is the lane line of identical geometry linear and former generation's lane line type of those lane lines is the car at crossing
The set of diatom；This crossing, refers to meet the set of the junction point of specified relationship, when this crossing generates, can adopt
Quickly generate by the method for labelling.
To sum up, it is an advantage of the current invention that:
Atomic layer data generation module, it is achieved that with less higher-quality data of memory storage so that both
When ensure that analog simulation vehicle motion flatness, can improve again vehicle coordinate, deflection calculate
Efficiency；
Hiberarchy data generation module, has automatically generated Intersection by specified relationship, not only facilitates
Management between different circuits, it is also possible to be automatically generated right-of-way allocation plan in road network, it is achieved by sky
Between the separation on time dimension of the circuit of the upper conflict of dimension；
The most important thing is, only need less input data volume (only needing lane line vector data), it is possible to complete
The most quickly generate all road network semantic data information, the information such as including section, junction point, crossing, pole
Big facilitates its application in virtual emulation, as the phase place of signal lights automatically generate, vehicle location coordinate
The quick acquisition etc. of deflection.
Accompanying drawing explanation
Fig. 1 is atomic layer data genaration step of the present invention；
Fig. 2 is hiberarchy data generation step of the present invention；
Fig. 3 is the road network semantic modeling system schematic towards vehicle groups animation；
Fig. 4 is lane line schematic diagram.
Detailed description of the invention
Detailed description of the invention is given below, in conjunction with accompanying drawing, the present invention is described in detail, but not as right
The restriction of the present invention.
Present invention aim to address that current road net model needs in vehicle animation simulation the most loaded down with trivial details
Data input and setting, and during vehicle animation simulation, obtain the problem that road net data is inefficient.The present invention
It is capable of only inputting lane line vector data (any representation, can coarse can be finely), it is possible to have
Imitate meets the data message required for vehicle animation simulation.
For reaching above-mentioned purpose, as depicted in figs. 1 and 2, the present invention provides a kind of towards vehicle groups animation
Road network Semantic Modeling Method, including:
Atomic layer data genaration step, carries out discretization recompression to input lane line vector data, and gives
Specify semantic information, obtain the semantic data of the lane line of broad sense, as atomic layer data；
Hiberarchy data generation step, calculates and stores volume between each Lane according to these atomic layer data and connect
The relation such as connect and adjoin, then generate this hiberarchy data, would know that signal according to this hiberarchy data
The information such as lamp distribution.
This atomic layer data genaration step includes:
Step 1, obtains lane line vector data
Step 2, uses curve fitting technique, such as spline function, multinomial to this lane line vector data
Approach, obtain the curve data of correspondence；
Step 3, uses Data Discretization compression algorithm such as Douglas-Peucker algorithm by this curve data
Obtain discrete broken line data；
Step 4, interrupts rule according to this lane line, and these discrete broken line data are interrupted and merged；
Step 5, carries out semantic information assignment by the node constituting this off-line broken line, generates the semanteme of lane line
Data, as atomic layer data.
When being embodied as, step 1 requires that the lane line of input is the car comprising crossing vehicle driving trace curve
Diatom.Further, if if input lane line vector data curve representation formula, then step 1 can directly be skipped.
These lane line broken line data interrupt and with the rule merged are:
Different lane line line styles in appointment threshold value are identical, move towards identical, and length is equal；
The out-degree and the in-degree that constitute the point range internal node of the discrete broken line of this lane line are respectively equal to 1.
This Lane requires to comprise:
Lane is used for describing the vehicle movement locus along circuit, as shown in Figure 4, not only includes practical significance
On road, also include the geometric locus additionally added at crossing for describing wheeled direction, respectively by it
Named circuit Lane and crossing Lane, is wherein crossing Lane in black dashed box line；
Lane curve uses the storage of discrete point range, meets " slickness " in certain threshold value, and slickness refers to
It is discrete point and the threshold value specified less than user with the distance between the curve of these matchings, this appointment threshold value
Referring to when animation simulation, this appointment threshold value is typically set to about 0.2 meter can meet slickness demand,
User, according to the demand degree decision appointment threshold size to data slickness, is worth the least, and the quality of data is the highest,
But required memory is the highest；On the contrary, being worth the biggest, the quality of data is the lowest, but required memory is the fewest.
The semantic information that this lane line discrete broken line node comprises includes: this node is away from the discrete broken line of this lane line
Starting point is along the total length of the discrete broken line of this lane line, the direction of the vector that this node and rear adjacent node are constituted
Angle.
This hiberarchy data generation step also includes as shown in Figure 2:
Step 6, based on above-mentioned atomic layer data, generates this layer according to the syntopy information between Lane
Link in aggregated(particle) structure data；
Step 7, based on above-mentioned atomic layer data, generates this layer according to the connection relation information between Lane
Connector in aggregated(particle) structure data；
Step 8, according to the overlapping relation generation group between information and the Lane of Link and Connector
Knit the intersection at crossing；
Step 9, can obtain Road according to the annexation between Link.
These road network Layer semantics data include:
Link: refer to former generation's Lane type with the Lane and these Lane of identical geometricshape
Lane for crossing.Syntopy information between Lane and Lane, two Lane tools are mainly described
Syntopy is had to refer to vehicle on a Lane by a lane-change (lane-change or to the right lane-change to the left),
Another Lane can be entered；
Connector, is used for describing the annexation between Link and Link, the two Link of connection
It is called the fromLink (front section of continuing) and toLink (follow-up section) of this Connector；
Intersection, refers to meet the set of the Connector of specified relationship R, is interpreted as intuitively
The set of the topological connection relation between all sections related at one crossing；
Wherein specified relationship R meets following condition:
Any two Connector a and b, if a and b has the toLink of common fromLink or common,
Then aRb；
Any two Connector a, b, c, if aRb, bRc set up, then aRc sets up；
Road is used for describing road, is made up of a series of connected Link and Intersection.
When being embodied as, the method for labelling can be used to quickly generate all of Intersection, it is to avoid one
Connector is repeatedly searched for, and the most once a Connector is retrieved, then make marks it, and search
Draw and this Connector has all Connector of specified relationship R, make marks the most accordingly, follow-up
Those Connector are skipped when searching this Connector.
As Fig. 3 the invention still further relates to a kind of road network semantic modeling system towards vehicle groups animation, shown bag
Include such as lower module:
Atomic layer data generation module, carries out discretization recompression to the lane line vector data of input, and composes
Give appointment semantic information, obtain atomic layer data Lane；
Hiberarchy data generation module, calculates and generates road network level language according to these atomic layer data Lane
Justice data.
This atomic layer data generation module also includes:
Input module, inputs lane line vector data；
Curve fitting module, for using curve fitting technique to obtain the curve number that lane line vector data is corresponding
According to；
Curve discretization module, for obtaining discrete broken line number by this curve employing Data Discretization compression algorithm
According to；
Interrupting merging module, interrupt rule according to this lane line, broken line data discrete to this lane line are carried out
Interrupt and merge；
Semantic assignment module, for carrying out semantic information assignment by the node constituting this lane line off-line broken line；
This hiberarchy data generation module, also includes:
Link generation module, generates in this hiberarchy data according to the syntopy information between Lane
Link；
Connector generation module, generates this hiberarchy data according to the connection relation information between Lane
In Connector；
Intersection generation module, according to the crossing pass between node and the information of junction point and Lane
System generates the Intersection at tissue crossing；
Road generation module, can obtain Road according to the annexation between Link, finally export road network
Data.
The described road network semantic modeling system towards vehicle groups animation, this lane line include regular vehicle diatom and
Road junction roadway line.
The described road network semantic modeling system towards vehicle groups animation, this interrupts rule and includes:
The line style of the average distance different lane lines in specifying threshold value is identical, move towards identical, and length is equal；
The out-degree and the in-degree that constitute the node within the point range of the discrete broken line of this lane line are respectively equal to 1.
The described road network semantic modeling system towards vehicle groups animation, the semantic information that this node comprises includes
From this lane line discrete broken line starting point along this broken line to the total length of this point and adjacent 2 vectors constituted
Deflection.
The described road network semantic modeling system towards vehicle groups animation, these atomic layer data are used for defining and raw
Becoming this hiberarchy data, this hiberarchy data includes, section, junction point, crossing, road；This section
Refer to that there is the lane line of identical geometry linear and former generation's lane line type of those lane lines is the car at crossing
The set of diatom；This crossing, refers to meet the set of the junction point of specified relationship, when this crossing generates, can adopt
Quickly generate by the method for labelling.
According to this atomic layer semantic data generation module above-mentioned and this hiberarchy data generation module, Ke Yiji
The big emulation facilitating vehicle animation, is first embodied in the renewal of vehicle coordinate position, it is only necessary to primary line
Property interpolation can be obtained by coordinate information and the deflection information of vehicle；Next is embodied in can be with full automatic life
Becoming the traffic phase place at crossing and the signal lamp arrangement scheme of acquiescence, those information is searched by reverse
Intersection → Connector → Link → Lane obtains.
Claims (10)
1. the road network Semantic Modeling Method towards vehicle groups animation, it is characterised in that include
Step 1, obtains lane line vector data；
Step 2, carries out curve fitting to this lane line vector data and obtains the curve data of correspondence；
Step 3, carries out this curve data Data Discretization compression and obtains the discrete folding that this curve data is corresponding
Line data；
Step 4, interrupts rule according to this lane line, and these discrete broken line data are interrupted and merged；
Step 5, carries out semantic information assignment by the node constituting this discrete broken line, obtains the semanteme of lane line
Data, as atomic layer data；
Step 6, based on these atomic layer data, according to the syntopy Automatic generation of information between this lane line
Section；
Step 7, based on these atomic layer data, automatically generates according to the connection relation information between this lane line
Junction point；
Step 8, automatically generates road according to the overlapping relation between this section and this junction point and this lane line
Mouthful；
Step 9, obtains road according to the annexation between this section.
2., as claimed in claim 1 towards the road network Semantic Modeling Method of vehicle groups animation, its feature exists
In, this lane line includes regular vehicle diatom and road junction roadway line.
3., as claimed in claim 1 towards the road network Semantic Modeling Method of vehicle groups animation, its feature exists
In, this interrupts rule and includes:
The line style of the average distance different lane lines in specifying threshold value is identical, move towards identical, and length is equal；
The out-degree and the in-degree that constitute the internal node of this discrete broken line point range are respectively equal to 1.
4., as claimed in claim 1 towards the road network Semantic Modeling Method of vehicle groups animation, its feature exists
In, the semantic information that this node comprises has: this node away from this discrete broken line starting point along the overall length of this discrete broken line
Degree, and the vectorial deflection that this node and rear adjacent node are constituted.
5., as claimed in claim 1 towards the road network Semantic Modeling Method of vehicle groups animation, its feature exists
In, these atomic layer data are used for defining and generating hiberarchy data, and this hiberarchy data includes, section,
Junction point, crossing, road；Section refers to the former generation of lane line and this lane line with identical geometry linear
Lane line type is the set of the lane line at crossing；This crossing, refers to meet the set of the junction point of specified relationship,
When this crossing generates, the method for labelling can be used to quickly generate.
6. the road network semantic modeling system towards vehicle groups animation, it is characterised in that include atom
Layer data generation module and hiberarchy data generation module, this atomic layer data generation module includes:
Data input module, is used for obtaining lane line vector data；
Curve fitting module, for this lane line vector data curve matching, obtaining the curve data of correspondence；
Curve discretization module, carries out this curve data Data Discretization compression and obtains this curve data correspondence
Discrete broken line data；
Interrupt merging module, interrupt rule according to this lane line, these discrete broken line data are interrupted and
Merge；
Semantic assignment module, obtains for the node constituting this lane line off-line broken line is carried out semantic information assignment
To lane line semantic data, as atomic layer data；
This hiberarchy data generation module includes:
Section generation module, based on these atomic layer data, according to the syntopy information between this lane line certainly
Dynamic generation section；
Junction point generation module, based on these atomic layer data, according to the connection relation information between this lane line
Automatically generate junction point；
Crossing generation module, automatic according to the overlapping relation between this section and this junction point and this lane line
Generate crossing；
Road generation module, obtains road according to the annexation between this section.
7., as claimed in claim 6 towards the road network semantic modeling system of vehicle groups animation, its feature exists
In, this lane line includes regular vehicle diatom and road junction roadway line.
8., as claimed in claim 6 towards the road network semantic modeling system of vehicle groups animation, its feature exists
In, this interrupts rule and includes:
The line style of the average distance different lane lines in specifying threshold value is identical, move towards identical, and length is equal；
The out-degree and the in-degree that constitute the node within the point range of the discrete broken line of this lane line are respectively equal to 1.
9., as claimed in claim 6 towards the road network semantic modeling system of vehicle groups animation, its feature exists
Include from total along this discrete broken line to this node of this discrete broken line starting point in, the semantic information that this node comprises
Length and the deflection of adjacent 2 vectors constituted.
10., as claimed in claim 6 towards the road network semantic modeling system of vehicle groups animation, its feature exists
In, these atomic layer data are used for defining and generate this hiberarchy data, and this hiberarchy data includes, road
Section, junction point, crossing, road；Lane line that this section refers to have identical geometry linear and this lane line
Former generation's lane line type is the set of the lane line at crossing；This crossing, refers to meet the junction point of specified relationship
Set, when this crossing generates, can use the method for labelling to quickly generate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410400159.2A CN104217580B (en) | 2014-08-14 | 2014-08-14 | Road network Semantic Modeling Method and system towards vehicle groups animation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410400159.2A CN104217580B (en) | 2014-08-14 | 2014-08-14 | Road network Semantic Modeling Method and system towards vehicle groups animation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104217580A CN104217580A (en) | 2014-12-17 |
| CN104217580B true CN104217580B (en) | 2016-08-17 |
Family
ID=52099015
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410400159.2A Active CN104217580B (en) | 2014-08-14 | 2014-08-14 | Road network Semantic Modeling Method and system towards vehicle groups animation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104217580B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104732591B (en) * | 2015-03-10 | 2017-08-25 | 中国科学院遥感与数字地球研究所 | The automatic generation method of three-dimensional virtual city |
| CN108447255B (en) * | 2018-03-21 | 2020-07-17 | 北方工业大学 | Urban road dynamic traffic network structure information system |
| CN111238504B (en) * | 2018-11-29 | 2023-04-07 | 沈阳美行科技股份有限公司 | Road segment modeling data generation method and device of road map and related system |
| CN110795822A (en) * | 2019-09-29 | 2020-02-14 | 郑州金惠计算机系统工程有限公司 | Traffic network semantic modeling method and device, electronic equipment and storage medium |
| CN112747758A (en) * | 2019-10-29 | 2021-05-04 | 北京百度网讯科技有限公司 | Road network modeling method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0026235D0 (en) * | 1999-10-26 | 2000-12-13 | France Telecom | A method of determining an itinery or route |
| US6233518B1 (en) * | 1998-07-28 | 2001-05-15 | Heung-Soo Lee | Method and system for providing an image vector-based traffic information |
| CN101290725A (en) * | 2007-04-17 | 2008-10-22 | 株式会社日立制作所 | Digital road map generation system and map generation method |
| CN102982703A (en) * | 2012-12-12 | 2013-03-20 | 成都合纵连横数字科技有限公司 | Interactive method of automobile driving simulator and virtual traffic environment simulation system |
| CN103531024A (en) * | 2013-10-28 | 2014-01-22 | 武汉旭云科技有限公司 | Dynamic traffic network urban road feature model and modeling method thereof |
-
2014
- 2014-08-14 CN CN201410400159.2A patent/CN104217580B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6233518B1 (en) * | 1998-07-28 | 2001-05-15 | Heung-Soo Lee | Method and system for providing an image vector-based traffic information |
| GB0026235D0 (en) * | 1999-10-26 | 2000-12-13 | France Telecom | A method of determining an itinery or route |
| CN101290725A (en) * | 2007-04-17 | 2008-10-22 | 株式会社日立制作所 | Digital road map generation system and map generation method |
| CN102982703A (en) * | 2012-12-12 | 2013-03-20 | 成都合纵连横数字科技有限公司 | Interactive method of automobile driving simulator and virtual traffic environment simulation system |
| CN103531024A (en) * | 2013-10-28 | 2014-01-22 | 武汉旭云科技有限公司 | Dynamic traffic network urban road feature model and modeling method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 虚拟群体与动态视频场景耳朵在线实时融合;张艺江 等;《计算机辅助设计与图形学学报》;20110131;第23卷(第1期);正文全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104217580A (en) | 2014-12-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104217580B (en) | Road network Semantic Modeling Method and system towards vehicle groups animation | |
| US11933627B2 (en) | High-definition map building method and apparatus | |
| CN108444482B (en) | Unmanned aerial vehicle autonomous road finding and obstacle avoiding method and system | |
| CN103699731B (en) | A kind of construction method of railway construction real scene model collaborative design platform | |
| CN110220521B (en) | High-precision map generation method and device | |
| CN108088456A (en) | A kind of automatic driving vehicle local paths planning method with time consistency | |
| CN103292816B (en) | Electronic map generating method, device and paths planning method, device | |
| CN102788584B (en) | The energy consumption prediction unit of road grade data generating device and generation method, vehicle console device and vehicle | |
| CN104573390B (en) | Space-time track fusion method based on cognitive law and road network topology generate method | |
| CN102538754B (en) | Method and equipment for calculating road gradient information | |
| CN111651829B (en) | Road simulation platform based on intelligent traffic flow simulation | |
| Wilkie et al. | Virtualized traffic at metropolitan scales | |
| CN106251331A (en) | The extracting method of atural object in a kind of inclination measurement scene | |
| CN104200127A (en) | Optimal path analyzing method based on road corner weight | |
| CN111539112B (en) | Scene modeling method for automatically driving vehicle to quickly search traffic object | |
| CN103218489A (en) | Method for simulating vehicle personalized driving characteristic based on video sample | |
| CN102927990B (en) | Locomotive is by the method for Geographic Information System determination urban road optimal path of automobile | |
| CN113312760A (en) | Traffic simulation-based networked motor vehicle right turn trajectory planning method and device | |
| JPWO2018016558A1 (en) | Simulation apparatus, simulation system, simulation method, and program | |
| CN114018239A (en) | Three-dimensional lane map construction method, device, equipment and storage medium | |
| CN104680782A (en) | Traffic control cloud system | |
| CN109741451A (en) | A kind of system that three-dimensional earth's surface BIM model is constructed based on topographic map | |
| CN110887503B (en) | Moving track simulation method, device, equipment and medium | |
| Lou et al. | The research on aggregate analysis of the regulatory detailed planning by considering the traffic capacity | |
| Li et al. | Improved lane-changing model for vanets in SUMO |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |